Will they blend? blog post series

Table of content

Will They Blend? Experiments in Data & Tool Blending. Today: Google Big Query meets SQLite. The Business of Baseball Games

Mon, 11/13/2017 - 10:16 admin

In this blog series we’ll be experimenting with the most interesting blends of data and tools. Whether it’s mixing traditional sources with modern data lakes, open-source devops on the cloud with protected internal legacy tools, SQL with noSQL, web-wisdom-of-the-crowd with in-house handwritten notes, or IoT sensor data with idle chatting, we’re curious to find out: will they blend? Want to find out what happens when IBM Watson meets Google News, Hadoop Hive meets Excel, R meets Python, or MS Word meets MongoDB?

Follow us here and send us your ideas for the next data blending challenge you’d like to see at willtheyblend@knime.com.

Today: Google Big Query meets SQLite. The Business of Baseball Games

Author: Dorottya Kiss, EPAM

The Challenge

They say if you want to know American society, first you have to learn baseball. As reported in a New York Times article, America had baseball even in times of war and depression, and it still reflects American society. Whether it is playing, watching, or betting on the games, baseball is in some way always connected to the lives of Americans.

According to Accuweather, different weather conditions play a significant role in determining the outcome of a baseball game. Air temperature influences the trajectory of the baseball; air density has an impact on the distance covered by the ball; temperature influences the pitcher’s grip; cloud coverage affects the visibility of the ball; and wind conditions - and weather in general - have various degrees of influence on the physical wellbeing of the players.

Another interesting article on Crowdhitter describes the fans’ attendance of the games and how this affects the home team’s success. Fan attendance at baseball games is indeed a key factor, in terms of both emotional and monetary support. So, what are the key factors determining attendance? On a pleasant day are they more likely to show up in the evening or during the day, or does it all just depend on the opposing team?

Some time ago we downloaded the data about attendance at baseball games for the 2016 season from Google’s Big Query Public data set and stored them on our own Google Big Query database. For the purpose of this blending experiment we also downloaded data about the weather during games from Weather Underground and stored these data on a SQLite database.

The goal of this blending experiment is to merge attendance data at baseball games from Google Big Query with weather data from SQLite. Since we have only data about one baseball season, it will be hard to train a model for reliable predictions of attendance. However, we have enough data for a multivariate visualization of the various factors influencing attendance.

Topic. Multivariate visual investigation of weather influence on attendance of baseball games.

Challenge. Blend attendance data from Google Big Query and weather data from SQLite.

Access Mode. Database Connector node with Simba 4.2 JDBC driver compatible with access to Google Big Query and dedicated SQLite Connector node.

Will They Blend? Experiments in Data & Tool Blending. Today: Finnish meets Italian and Portuguese through the Google Translate API. Preventing weather from getting lost in translation

Mon, 10/09/2017 - 11:18 admin

In this blog series we’ll be experimenting with the most interesting blends of data and tools. Whether it’s mixing traditional sources with modern data lakes, open-source devops on the cloud with protected internal legacy tools, SQL with noSQL, web-wisdom-of-the-crowd with in-house handwritten notes, or IoT sensor data with idle chatting, we’re curious to find out: will they blend? Want to find out what happens when IBM Watson meets Google News, Hadoop Hive meets Excel, R meets Python, or MS Word meets MongoDB?

Follow us here and send us your ideas for the next data blending challenge you’d like to see at willtheyblend@knime.com.

Today: Finnish meets Italian and Portuguese through the Google Translate API. Preventing weather from getting lost in translation

Will They Blend? Experiments in Data & Tool Blending. Today: SugarCRM meets Salesforce. Crossing Accounts and Opportunities

Mon, 09/25/2017 - 10:30 RolandBurger

In this blog series we’ll be experimenting with the most interesting blends of data and tools. Whether it’s mixing traditional sources with modern data lakes, open-source devops on the cloud with protected internal legacy tools, SQL with noSQL, web-wisdom-of-the-crowd with in-house handwritten notes, or IoT sensor data with idle chatting, we’re curious to find out: will they blend? Want to find out what happens when IBM Watson meets Google News, Hadoop Hive meets Excel, R meets Python, or MS Word meets MongoDB?

Follow us here and send us your ideas for the next data blending challenge you’d like to see at willtheyblend@knime.com.

Today: SugarCRM meets Salesforce. Crossing Accounts and Opportunities

The Challenge

Businesses use Customer Relationship Management (CRM) systems to keep track of all their customer related activities – creating leads and opportunities, managing contacts and accounts, sending quotes and invoices, etc. As long as it is somehow related to the stream of revenue, it is (or at least should be) stored in a CRM system.

Since there is more than one CRM solution on the market, there is a distinct chance that your organization uses multiple CRM platforms. While there might be sound reasons for this, it also poses a significant challenge: How do you combine data from several platforms? How do you generate a single, consolidated report that shows you how well the sales activities of your whole company are going?

One option is to export some tables, fire up your spreadsheet software of choice, and paste the stuff together. Then do the same thing next week. And the week after. And the week after that one (you get the point). Doesn’t sound too enticing? Fear not! This is KNIME, and one of our specialties is to save you the frustration of doing things manually. Fortunately, both SugarCRM and Salesforce allow their users to access their services via REST API, and that is exactly what we are going to do in this blog post.

There are a couple of prerequisites here. First of all, you obviously need accounts for SugarCRM and Salesforce. If you don’t have them but still want to try this yourself, you’ll be happy to see that both companies offer free trial licenses:

https://info.sugarcrm.com/trial-crm-software.html?utm_source=crmsoftware&utm_medium=referral&utm_campaign=crmsoftware-review

https://developer.salesforce.com/signup

You can learn more about how to use the REST APIs of SugarCRM and Salesforce here:

http://support.sugarcrm.com/Documentation/Sugar_Developer/Sugar_Developer_Guide_7.9/Integration/Web_Services/v10/

https://developer.salesforce.com/docs/atlas.en-us.api_rest.meta/api_rest/intro_what_is_rest_api.htm

Topic. Get a consolidated view of all customer data from two separate platforms

Challenge. Query data from SugarCRM and Salesforce via their APIs

Access Mode. KNIME REST Web Services

Will They Blend? Experiments in Data & Tool Blending. Today: A Recipe for Delicious Data: Mashing Google and Excel Sheets

Mon, 07/24/2017 - 10:47 amartin

In this blog series we’ll be experimenting with the most interesting blends of data and tools. Whether it’s mixing traditional sources with modern data lakes, open-source devops on the cloud with protected internal legacy tools, SQL with noSQL, web-wisdom-of-the-crowd with in-house handwritten notes, or IoT sensor data with idle chatting, we’re curious to find out: will they blend? Want to find out what happens when IBM Watson meets Google News, Hadoop Hive meets Excel, R meets Python, or MS Word meets MongoDB?

Follow us here and send us your ideas for the next data blending challenge you’d like to see at willtheyblend@knime.com.

Today: A Recipe for Delicious Data: Mashing Google and Excel Sheets

Will They Blend? Experiments in Data & Tool Blending. Today: OCR on Xerox Copies meets Semantic Web. Have Evolutionary Theories changed?

Mon, 07/03/2017 - 11:04 Dario Cannone

In this blog series we’ll be experimenting with the most interesting blends of data and tools. Whether it’s mixing traditional sources with modern data lakes, open-source devops on the cloud with protected internal legacy tools, SQL with noSQL, web-wisdom-of-the-crowd with in-house handwritten notes, or IoT sensor data with idle chatting, we’re curious to find out: will they blend? Want to find out what happens when IBM Watson meets Google News, Hadoop Hive meets Excel, R meets Python, or MS Word meets MongoDB?

Will They Blend? Experiments in Data & Tool Blending. Today: Teradata Aster meets KNIME Table. What is that chest pain?

Mon, 04/24/2017 - 11:41 knime_admin

In this blog series we’ll be experimenting with the most interesting blends of data and tools. Whether it’s mixing traditional sources with modern data lakes, open-source devops on the cloud with protected internal legacy tools, SQL with noSQL, web-wisdom-of-the-crowd with in-house handwritten notes, or IoT sensor data with idle chatting, we’re curious to find out: will they blend? Want to find out what happens when IBM Watson meets Google News, Hadoop Hive meets Excel, R meets Python, or MS Word meets MongoDB?

Follow us here and send us your ideas for the next data blending challenge you’d like to see at willtheyblend@knime.com.

Today: Teradata Aster meets KNIME Table. What is that chest pain?

Will They Blend? Experiments in Data & Tool Blending. Today: Blending Databases. A Database Jam Session

Mon, 04/10/2017 - 15:19 rs

In this blog series we’ll be experimenting with the most interesting blends of data and tools. Whether it’s mixing traditional sources with modern data lakes, open-source devops on the cloud with protected internal legacy tools, SQL with noSQL, web-wisdom-of-the-crowd with in-house handwritten notes, or IoT sensor data with idle chatting, we’re curious to find out: will they blend? Want to find out what happens when IBM Watson meets Google News, Hadoop Hive meets Excel, R meets Python, or MS Word meets MongoDB?

Follow us here and send us your ideas for the next data blending challenge you’d like to see at willtheyblend@knime.com.

Today: Blending Databases. A Database Jam Session

Will They Blend? Experiments in Data & Tool Blending. Today: YouTube Metadata meet WebLog Files. What will it be tonight – a movie or a book?

Mon, 03/27/2017 - 14:31 knime_admin

In this blog series we’ll be experimenting with the most interesting blends of data and tools. Whether it’s mixing traditional sources with modern data lakes, open-source devops on the cloud with protected internal legacy tools, SQL with noSQL, web-wisdom-of-the-crowd with in-house handwritten notes, or IoT sensor data with idle chatting, we’re curious to find out: will they blend? Want to find out what happens when IBM Watson meets Google News, Hadoop Hive meets Excel, R meets Python, or MS Word meets MongoDB?

Follow us here and send us your ideas for the next data blending challenge you’d like to see at willtheyblend@knime.com.

Today: YouTube Metadata meet WebLog Files. What will it be tonight – a movie or a book?

Will They Blend? Experiments in Data & Tool Blending. Today: Kindle epub meets image JPEG: Will KNIME make peace between the Capulets and the Montagues?

Mon, 03/13/2017 - 10:06 knime_admin

In this blog series we’ll be experimenting with the most interesting blends of data and tools. Whether it’s mixing traditional sources with modern data lakes, open-source devops on the cloud with protected internal legacy tools, SQL with noSQL, web-wisdom-of-the-crowd with in-house handwritten notes, or IoT sensor data with idle chatting, we’re curious to find out: will they blend? Want to find out what happens when IBM Watson meets Google News, Hadoop Hive meets Excel, R meets Python, or MS Word meets MongoDB?

Follow us here and send us your ideas for the next data blending challenge you’d like to see at willtheyblend@knime.com.

Today: Kindle epub meets image JPEG: Will KNIME make peace between the Capulets and the Montagues?

Will They Blend? Experiments in Data & Tool Blending. Today: SAS, SPSS, and MATLAB meet Amazon S3: setting the past free

Mon, 02/27/2017 - 11:16 phil

In this blog series we’ll be experimenting with the most interesting blends of data and tools. Whether it’s mixing traditional sources with modern data lakes, open-source devops on the cloud with protected internal legacy tools, SQL with noSQL, web-wisdom-of-the-crowd with in-house handwritten notes, or IoT sensor data with idle chatting, we’re curious to find out: will they blend? Want to find out what happens when IBM Watson meets Google News, Hadoop Hive meets Excel, R meets Python, or MS Word meets MongoDB?